AIMC Topic: Ligands

Clear Filters Showing 471 to 480 of 660 articles

Knowledge-Based Conformer Generation Using the Cambridge Structural Database.

Journal of chemical information and modeling Feb 22, 2018
Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geomet...
Models, Molecular Knowledge Bases Ligands Macrocyclic Compounds Hydrogen Bonding Databases, Chemical Molecular Conformation Algorithms Proteins Databases, Protein Software
View on PubMed DOI

Statistical and machine learning approaches to predicting protein-ligand interactions.

Current opinion in structural biology Feb 20, 2018
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to des...
Protein Binding Computational Biology Models, Statistical Proteins Humans Machine Learning Algorithms Ligands Quantitative Structure-Activity Relationship
View on PubMed DOI

Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families.

ChemMedChem Feb 5, 2018
Computational models for predicting the activity of small molecules against targets are now routinely developed and used in academia and industry, partially due to public bioactivity databases. While models based on bigger datasets are the trend, rec...
Humans Machine Learning Cytochrome P-450 Enzyme System Receptors, Cytoplasmic and Nuclear Small Molecule Libraries Algorithms Cytochrome P-450 Enzyme Inhibitors Databases, Factual Ligands
View on PubMed DOI

Boosted neural networks scoring functions for accurate ligand docking and ranking.

Journal of bioinformatics and computational biology Feb 4, 2018
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order p...
Ligands Escherichia coli Proteins tRNA Methyltransferases Proteins Databases, Protein Structure-Activity Relationship Neural Networks, Computer Machine Learning Drug Design Protein Binding Algorithms Molecular Docking Simulation
View on PubMed DOI

Development of CDK-targeted scoring functions for prediction of binding affinity.

Biophysical chemistry Feb 1, 2018
Cyclin-dependent kinase (CDK) is an interesting biological macromolecule due to its role in cell cycle progression, transcription control, and neuronal development, to mention the most studied biological activities. Furthermore, the availability of h...
Cyclin-Dependent Kinases Machine Learning Ligands Binding Sites
View on PubMed DOI

Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

Computational and mathematical methods in medicine Jan 30, 2018
We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or si...
Binding Sites Amino Acid Motifs Sequence Analysis, Protein Software Quercetin Algorithms Reproducibility of Results Humans Protein Binding Ligands ROC Curve False Positive Reactions Machine Learning Receptors, G-Protein-Coupled Area Under Curve
View on PubMed DOI

K: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.

Journal of chemical information and modeling Jan 29, 2018
Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning appro...
Protein Binding Computational Biology Drug Discovery Deep Learning Models, Chemical Ligands Proteins Databases, Protein Structure-Activity Relationship
View on PubMed DOI

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

PLoS computational biology Jan 8, 2018
This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules...
Protein Interaction Mapping Ligands Nucleic Acids Static Electricity Databases, Protein Area Under Curve Molecular Dynamics Simulation Neural Networks, Computer Machine Learning Algorithms Models, Neurological Proteins Protein Binding Computational Biology Humans
View on PubMed DOI

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Journal of chemical information and modeling Nov 27, 2017
Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with ma...
Drug Discovery Structure-Activity Relationship Models, Biological Ligands Humans Machine Learning Computer Simulation
View on PubMed DOI

Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.

Journal of computer-aided molecular design Nov 13, 2017
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org...
Protein Binding Molecular Dynamics Simulation Ligands Molecular Docking Simulation Protein Conformation Computer-Aided Design Humans Receptors, Cytoplasmic and Nuclear Drug Design Neural Networks, Computer Binding Sites Machine Learning
View on PubMed DOI
Popular Topics
Recent Journals